Medical image diagnosis allows body information to be obtained noninvasively and thus has been widely performed in recent years. Three-dimensional images obtained by various types of image diagnosis apparatuses such as x-ray computer tomography (CT) apparatuses, magnetic resonance imaging (MRI) apparatuses, positron emission tomography (PET) apparatuses, and single photon emission computed tomography (SPECT) apparatuses have been used in diagnosis or follow-up.
An image obtained by such a medical image diagnosis apparatus is viewed for reading, simply and can also obtain various information items through an image process. For example, in the X-ray CT apparatus, since a volume image having a high spatial resolution can be obtained, images of a body part, a blood vessel, or the like are extracted by a segmentation technology, and it is possible to three-dimensional visualization these images by a volume rendering method. Furthermore, the images can be visualized simply, and it is possible to quantitatively evaluate the images by extracting a disease site such as a tumor using various image process algorithms to obtain a maximum diameter or a volume of the images. In the related art, as a system for aiding a medical image diagnosis, a computer aided diagnosis (CAD) is proposed. When the CAD is finely functional classified, the CAD is divided into a computer aided detection (CADe) and a computer aided diagnosis (CADx). In the CADe, a candidate position in which a focus of disease is present on an image is automatically detected by a computer. The CADe has a function for marking the position and aids in pointing out the lesion. On the other hand, the CADx has a function for output a numerical value of physical characteristics relating to the lesion candidate (maximum diameter, volume, or the like), a malignancy differentiation, or data or numerical value of the degree of progress of the lesion in addition to the CADe function. The CADx outputs qualitative and quantitative data of the lesion to aid the diagnosis. Among them, a CADe system for a lung cancer and breast cancer has been commercialized, and its importance increases.
On the other hand, the medical image is used in not only the diagnosis but also the treatment. Specifically, the importance of the image in a radiation treatment increases. The radiation treatment is mainly subjected in four steps a diagnosis, a treatment plane, a treatment, and a follow-up. An image or an image process technology is used in each step. In bed positioning that is an important process in the treatment, in order to obtain positioning with higher accuracy than a two-dimensional image which is performed in the related art, an image guided radiation therapy (IGRT) using a three-dimensional image is performed.
The information obtained from such a medical image and an image process is used in each scene of the examination such as a diagnosis, a treatment, or the like. The major factor of the usefulness thereof is that the image includes various explicit and implicit information items and complementary information for complementing the information from a plurality of images can be obtained. For example, a pixel value of the X-ray CT image, a so-called CT value is obtained by imaging X-ray absorption characteristics of a living body, and the value can be recognized by comparing other physical property value of the living body. In addition, the images obtained from the X-ray CT apparatus and the PET apparatus are called a foam image and a functional image, respectively. Since, just as their name says, in the CT image, the shape of the living body is clear, and with respect to this, the PET image can recognize a function of the living body such as a glycometabolism or an amino-acid metabolism, it is possible to medical determine using the information between the CT image and the PET image. For effectively using the medical images in the examination, in PTL 1, a method for constructing a three-dimensional bio data model for a surgical simulation is proposed.